World Leaders
Military Serviece
and Propensity for War

MAJ Dusty Turner

Grant
Grant

Kennedy
Kennedy

Nixon
Nixon

Bush
Bush

Grant
Grant

Kennedy
Kennedy

Nixon
Nixon

Bush
Bush

Disclaimer

The research presented in this document does not represent the official views of the Army. The opinions and findings expressed herein are solely those of the authors or contributors and do not reflect Army policy or endorsement.

Are leaders with military experience more likely to lead their countries in war?

But first

Why are you at Baylor and why are you trying to answer this question…?

Army

  • Engineer Officer
    • Training: Fort Leonard Wood Missouri
    • Platoon Leader: Hawaii (Iraq)
    • Company Commander: White Sands Missile Range, NM (Afghanistan)
  • Assistant Professor / Instructor
    • United States Military Academy, West Point, NY
  • Operations Research Systems Analyst (ORSA)
    • Center for Army Analysis: Fort Belvoir, VA

Baylor

  • Dr (COL R) Rodney Sturdivant

  • Applied Logistic Regression

  • Associate Professor and Director of the Statistical Consulting Center

  • Dancing with the Waco Stars

Dr Peter Campbell

  • Associate Professor of Political Science at Baylor University
  • PhD in Political Science from the University of Notre Dame
  • Author of “Military Realism: The Logic and Limits of Force and Innovation in the US Army
  • Expert in:
    • Civil-military relations
    • Insurgency and counterinsurgency
    • The just war tradition
    • Cyber warfare

Are leaders with military
experience more likely to
lead their countries in war?

Now, back to the point…

Those with listed experience

Now, back to the point…

Those without listed experience

What I assume about you…

Insert some image or gif

What I assume about you…

Statistics

R

Real World Question

What I assume about you…

Statistics

\(\log\left(\frac{p}{1-p}\right) = \beta_0 + \beta_1X_1 + \beta_2X_2 + \ldots + \beta_kX_k\)

What I assume about you…

Statistics

R

Real World Question

What I assume about you…

R

glm(formula = y ~ x, family = binomial(link = "logit"))

What I assume about you…

Statistics

R

Real World Question

What I assume about you…

Real World Question

Grant Bush Nixon Kennedy

What I assume about you…

Real World Question

Grant Bush Nixon Kennedy

What I assume about you…

Statistics

R

Real World Question

Factors to consider

personal
age
gender
leveledu
married
marriedinpower
divorced
totalspouses
childtotal
orphanbinary
illegit
military
milnoncombat
combat
warwin
warloss
rebel
rebelwin
rebelloss
police
militarcareer
professional
teacher
journalism
law
medicine
religion
activist
creative
business
bluecollar
scienceeng
political
careerpolitician
aristocratlandowner
royalty
officetenure1000

Explore

Explore

Those with listed experience

Explore

Those without listed experience

Model Building Techniques

  • Forward Selection
  • Backwards Selection
  • All Subsets
  • Purposeful Selection

Purposeful Selection

  • Univariable Analysis

  • Initial Multivariable Model

  • Compare Models

  • Preliminary Main Effects Model

  • Main Effects Model

  • Preliminary Final Model

  • Adequacy and Model Fit

Purposeful Selection



  • Univariable Analysis
  • Adequacy and Model Fit

Univariable Analysis

univariable <- function(factor) {

  formula <- str_c("cwinit", "~", factor)
  
  glm(formula, data = data, family = binomial(link = "logit")) |> 
  broom::tidy() |> 
  select(term, estimate, p.value) |> 
  filter(term != "(Intercept)")

}

Univariable Analysis

univariable <- function(factor) {

  formula <- str_c("cwinit", "~", factor)
  
  glm(formula, data = data, family = binomial(link = "logit")) |> 
  broom::tidy() |> 
  select(term, estimate, p.value) |> 
  filter(term != "(Intercept)")

}

Univariable Analysis

univariable <- function(factor) {

  formula <- str_c("cwinit", "~", factor)
  
  glm(formula, data = data, family = binomial(link = "logit")) |> 
  broom::tidy() |> 
  select(term, estimate, p.value) |> 
  filter(term != "(Intercept)")

}

Univariable Analysis

univariable <- function(factor) {

  formula <- str_c("cwinit", "~", factor)
  
  glm(formula, data = data, family = binomial(link = "logit")) |> 
  broom::tidy() |> 
  select(term, estimate, p.value) |> 
  filter(term != "(Intercept)")

}

Univariable Analysis

future_map_dfr(
  .x = factors_of_interest,
  .f = ~univariable(factor = .x),
  .progress = T
)


univariable(factor = "age") |> 
  gt::gt() |> 
  gt::fmt_number()
term estimate p.value
age 0.01 0.00

Univariable Analysis

term estimate p.value odds
cap_1 12.79 0.00 357,207.90
contigld 3.73 0.00 41.59
monadicleaderrisk 6.52 0.00 675.98
cap_2 10.77 <0.01 47,782.45
year −0.01 <0.01 0.99
syscon 9.72 <0.01 16,591.53
leadernoinit −0.20 <0.01 0.82
defpact 1.13 <0.01 3.10
satisdy 1.63 <0.01 5.09
warwin 0.89 <0.01 2.44
rebelloss 1.21 <0.01 3.34
leadernoinit2 −0.01 <0.01 0.99
countryrandom 0.49 <0.01 1.64
leaderpeaceyrs1 <0.01 <0.01 1.00
combat 0.48 <0.01 1.62
leaderpeaceyrs2 <0.01 <0.01 1.00
cwpceyrs1 <0.01 <0.01 1.00
leaderpeaceyrs3 <0.01 <0.01 1.00
leadernoinit3 0.00 <0.01 1.00
sideabof 0.53 <0.01 1.70
cwpceyrs2 <0.01 <0.01 1.00
rebel 0.33 <0.01 1.39
cwpceyrs3 <0.01 <0.01 1.00
rebelwin 0.46 <0.01 1.58
careerpolitician −0.32 <0.01 0.73
religion 0.74 <0.01 2.10
age 0.01 <0.01 1.01
warloss 0.40 <0.01 1.49
marriedinpower −0.38 <0.01 0.69
business −0.44 <0.01 0.64
medicine −0.87 <0.01 0.42
militarycareer 0.24 <0.01 1.27
milnoncombat 0.30 <0.01 1.35
polity21 −0.01 <0.01 0.99
numGPs 0.08 <0.01 1.09
divorced −0.24 <0.01 0.79
teacher 0.19 <0.01 1.21
parstability 0.25 <0.01 1.28
totalspouses −0.06 <0.01 0.94
dem1 −0.15 <0.01 0.86
demlow −0.01 <0.01 0.99
married −0.31 <0.01 0.74
jointdem −0.25 <0.01 0.78
activist 0.14 <0.01 1.15
illegit −0.36 <0.01 0.70
childtotal −0.01 0.03 0.99
leveledu −0.05 0.03 0.95
gender 0.34 0.07 1.41
aristocratlandowner −0.14 0.07 0.87
dem2 0.06 0.17 1.07
law −0.07 0.19 0.93
police −0.26 0.26 0.77
orphanbinary −0.14 0.28 0.87
officetenure1000 −0.01 0.41 0.99
bluecollar −0.04 0.50 0.96
royalty −0.04 0.52 0.96
scienceeng 0.04 0.70 1.04
fatalmid 22.13 0.77 4,077,739,297.16
demhigh <0.01 0.78 1.00
force2dv 23.28 0.81 12,894,882,352.48
random <0.01 0.85 1.01
dyadid <0.01 0.96 1.00
journalism <0.01 0.99 1.00
creative 0.00 1.00 1.00

Final Model

term estimate std.error statistic p.value odds_ratio
(Intercept) −3.71 0.28 −13.10 <0.01 0.02
log_yearssincemidinit −1.16 0.06 −17.88 <0.01 0.31
illegit1 −0.56 0.21 −2.68 <0.01 0.57
medicine1 −0.45 0.25 −1.84 0.07 0.64
royalty1 −0.36 0.14 −2.69 <0.01 0.69
militarycareer1 −0.26 0.15 −1.72 0.09 0.77
age 0.01 <0.01 2.35 0.02 1.01
officetenure1000 0.03 0.01 2.20 0.03 1.03
parstability1 0.32 0.18 1.77 0.08 1.37
yearssincemidinit 0.35 0.06 5.57 <0.01 1.42
combat1 0.54 0.14 3.74 <0.01 1.71
creative1 0.56 0.27 2.03 0.04 1.74
rebelloss1 0.68 0.25 2.67 <0.01 1.97
milnoncombat1 0.70 0.18 3.94 <0.01 2.01

Final Model

What else should I show?

Hosmer Lemeshow Test

Leader Risk Score

Outliers

So What?

  • Should we never elect a leader with military service?
  • Is this causal?